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Mid LevelFull-timeC++Machine LearningPyTorchML
Salary Range
$189,000 - $258,000 /year
About the Role
The Perception team is pioneering the development of a multi-modality foundation model to drive the next generation of autonomous system intelligence. As a Multi-modality Foundation Model Engineer, you will focus on building highly efficient, production-ready multi-modality models.
Key Responsibilities
- →Build, pre-train, and evaluate large-scale multi-modality foundation models from the ground up, successfully aligning diverse data streams (e.g., Vision, LiDAR, Radar, Language, Audio).
- →Define and execute the ML roadmap for deploying these multi-modality representations to the vehicle.
- →Architect and implement Knowledge Distillation pipelines to compress large-capacity multi-modal teacher models into highly efficient, production-ready student models.
- →Build high-quality training and evaluation datasets, applying advanced data-centric techniques to maximize cross-modal representation learning and student model convergence.
- →Collaborate with downstream perception teams to integrate and validate the performance, robustness, and latency of your models in on-board production systems.
Requirements
- ✓MS or PhD in Computer Science, Machine Learning, or a related technical field with demonstrated professional experience.
- ✓Deep, proven expertise in building and training large-scale multi-modality foundation models (e.g., Vision-Language Models (VLMs), Vision-Audio-Text, or Vision-LiDAR-Radar architectures).
- ✓Strong understanding of cross-modal alignment, multi-modal attention mechanisms, and large-scale pre-training techniques.
- ✓Proven experience in Knowledge Distillation (KD), model compression, and training highly efficient student models for production environments.
- ✓Proficiency in ML frameworks (e.g., PyTorch) and experience building large-scale ML training and evaluation pipelines.
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